A2.html

import heapq

def dijkstra(graph, start,end) :
    # Priority queue to store (distance, node)
    queue = [(0, start)]
    distances = {node: float('inf') for node in graph}
    distances[start] = 0
    previous_nodes = {node: None for node in graph}

    while queue:
        current_distance, current_node = heapq.heappop(queue)

        # If we reached the destination, reconstruct the path
        if current_node == end:
            path = []
            while current_node:
                path.append(current_node)
                current_node = previous_nodes[current_node]
            return path[::-1], distances[end]

        if current_distance > distances[current_node]:
            continue

        for neighbor, weight in graph[current_node].items():
            distance = current_distance + weight

            # If found shorter path to neighbor
            if distance < distances[neighbor]:
                distances[neighbor] = distance
                previous_nodes[neighbor] = current_node
                heapq.heappush(queue, (distance, neighbor))

    return None, float('inf')

# Example usage:
graph = {
    'A': {'B': 1, 'C': 4},
    'B': {'A': 1, 'C': 2, 'D': 5},
    'C': {'A': 4, 'B': 2, 'D': 1},
    'D': {'B': 5, 'C': 1}
}

path, distance = dijkstra(graph, 'A', 'D')
print(f"Shortest path: {path}")
print(f"Distance: {distance}")